The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models
Open accessAbstract
We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a first tumor detection is performed, based on selecting asymmetric areas with respect to the approximate brain symmetry plane and fuzzy classification. Its result constitutes the initialization of a segmentation method based on a combination of a deformable model and spatial relations, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy models. The results obtained on different types of tumors have been evaluated by comparison with manual segmentations.
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Cites
Snakes, shapes, and gradient vector flow
Xu, C., & Prince, J. L. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661186 · Cited by 4,001
Image matching as a diffusion process: an analogy with maxwell’s demons
Thirion, J.-P. (1998) · Medical Image Analysis · doi:10.1016/s1361-8415(98)80022-4 · Cited by 2,445
Cited by
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
A survey of MRI-based medical image analysis for brain tumor studies
Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/13/r97 · Cited by 1,036
State of the art survey on MRI brain tumor segmentation
Gordillo, N., Montseny, E., & Sobrevilla, P. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.05.002 · Cited by 697
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., & Bourgeat, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2895894 · Cited by 298
Metadata
- Authors
- Khotanlou, H., Colliot, O., Atif, J., & Bloch, I.
- Venue
- Fuzzy Sets and Systems
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 160
- Issue
- 10
- Pages
- 1457-1473
- ISSN
- 0165-0114 (ISSN-L)
- OpenAlex
- W2116654471
- Cited by
- 261
- References
- 2
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- green
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- Khotanlou, H., Colliot, O., Atif, J., & Bloch, I. (2009). 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets and Systems. https://doi.org/10.1016/j.fss.2008.11.016
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
